What is the Era of the Intelligent Insurer

Publié le par hanswillertbleuazurconsultingeu

The biggest innovations in insurance over the next three years will not be in the technology tools themselves, but in how we design them with employees, customers, intermediaries and other human partners in mind.

Digital technology continues to reshape the insurance industry at an unprecedented and quickening pace. In our Technology Vision 2017 research, 87 percent of insurance respondents agreed that we have entered an era of technology advancement that is no longer marked by linear progression, but by an exponential rate of change.

What sets this new wave of disruption apart from those that preceded it is that humans are firmly in control of how technology reshapes our experiences, our industry and the wider world. It’s no longer people who are adapting to technology—rather, the technology is adapting to us.

We’re putting technology to work to disrupt ourselves, our organizations and entire industries. The technology we use today—compared to that of just a few years ago—is increasingly interactive, as touch displays, mixed reality, and natural language processing make it feel more human.

Advanced technology is now capable of learning, with contextual analysis, image recognition and deep learning algorithms that make it seem to think more like us. And, perhaps best of all, technology can now adapt—by constantly aligning itself to our wants and needs.

The five major trends observed are :

  1. AI is the new UI – The Experience Above All
  2. Ecosystem Power Plays – Unleash the Power of Us
  3. Workforce Marketplace – Invent Your Future
  4. Design for Humans – Inspire New Behaviors
  5. The Uncharted – Invent New Industries, Set New Standards

Marketplaces

Click here to access Accenture’s detailed report

The Imperative to Raise Enterprise Risk Intelligence

Publié le par hanswillertbleuazurconsultingeu

How to raise enterprise risk intelligence

  • Break down silos and collaborate. To ensure all risks are addressed, finance, operations, compliance, legal and IT functions should work together in managing enterprise risks. According to 53 percent of respondents, there is little, if any, collaboration among these functions to achieve a clearly defined enterprise risk management strategy.
  • Focus on accomplishments that will make a difference. The findings reveal a significant gap between the most important features of a risk intelligence platform and what features are actually accomplished. The features considered most important but rarely accomplished are:
    • Business continuity response (produces plans, runs business impact analyses, resiliency controls and engages stakeholders in crisis drills and recovery)
    • Incident/issue risk response (coordination of classification, collaboration, evidence, policies and reporting across the organization for all operational and security risk events)
    • Operational risk & compliance (creates risk registers and runs Risk and Compliance Self-Assessments (RCSAs) against critical business processes to report key risk indicators (KRIs), findings and loss events)
    • Threat and vulnerability mitigation (automates continuous risk correlation, prioritization and remediation of assets and operation criticality, threat reachability, control and vulnerabilities)
  • Establish a formal budget for enterprise risk management. It is critical to allocate resources specifically designated to achieving a well-executed enterprise risk management program. Fiftyeight percent of respondents say their organizations do not have a formal budget.
  • Engage management and the board of directors in the organization’s risk strategy. The inability to get started was one of the top three barriers to achieving risk management objectives. Senior leadership’s involvement will incentivize and motivate collaboration and a formal process for achieving the objectives of a risk management program.
  • Achieve clarity of your IT assets and infrastructure. A clear map of the infrastructure and categorization of assets, especially high value and knowledge assets, is key to ensuring appropriate risk measures are in place. Only 24 percent of respondents say they have categorized assets based on their business criticality.
  • Assign accountability for the achievement of specific risk management objectives. According to the findings, either no one person has overall responsibility or it is dispersed throughout the organization.
  • Measure effectiveness in risk intelligence efforts. Only 31 percent of respondents say their organizations have specific metrics to determine how well risks are being managed. Many organizations represented in this study are not measuring such key objectives as time to contain threats and attacks, time to identify and pinpoint high-risk areas and time to remediate after containment of the attack.
  • Consolidated risk reporting is essential. Sixty-three percent of respondents say it is essential or very important to have a centralized or consolidated risk reporting (one set of metrics) in order to achieve a strong security posture.
  • Replace complexity with ease of use. The number one barrier to achieving risk management objectives is the complexity of technologies that support risk management objectives. Understandably, the number one feature of a risk management solution is ease of use (53 percent of respondents). Investments in risk management technologies that end up on the shelf because of complexity and the lack of in-house expertise will frustrate any attempts to achieve an enterprise risk management program.

ERM Survey

2017_Report_on_ERM

A Field Guide to Data Science

Publié le par hanswillertbleuazurconsultingeu
  • Data Science is the art of turning data into actions.

It’s all about the tradecraft. Tradecraft is the process, tools and technologies for humans and computers to work together to transform data into insights.

  • Data Science tradecraft creates data products.

Data products provide actionable information without exposing decision makers to the underlying data or analytics (e.g., buy/sell strategies for financial instruments, a set of actions to improve product yield, or steps to improve product marketing).

  • Data Science supports and encourages shifting between deductive (hypothesis-based) and inductive (patternbased) reasoning.

This is a fundamental change from traditional analysis approaches. Inductive reasoning and exploratory data analysis provide a means to form or refine hypotheses and discover new analytic paths. Models of reality no longer need to be static. They are constantly tested, updated and improved until better models are found.

  • Data Science is necessary for companies to stay with the pack and compete in the future.

Organizations are constantly making decisions based on gut instinct, loudest voice and best argument – sometimes they are even informed by real information. The winners and the losers in the emerging data economy are going to be determined by their Data Science teams.

  • Data Science capabilities can be built over time.

Organizations mature through a series of stages – Collect, Describe, Discover, Predict, Advise – as they move from data deluge to full Data Science maturity. At each stage, they can tackle increasingly complex analytic goals with a wider breadth of analytic capabilities. However, organizations need not reach maximum Data Science maturity to achieve success. Significant gains can be found in every stage.

  • Data Science is a different kind of team sport.

Data Science teams need a broad view of the organization. Leaders must be key advocates who meet with stakeholders to ferret out the hardest challenges, locate the data, connect disparate parts of the business, and gain widespread buy-in.

Data Science Activities

2015-field-guide-to-data-science-160211215115

What’s now and next in Analytics, AI, and Automation

Publié le par hanswillertbleuazurconsultingeu

Over the past few years, rapid technological advances in digitization and data and analytics have been

  • reshaping the business landscape,
  • supercharging performance
  • and enabling the emergence of new business innovations
  • and new forms of competition
  • and business disruption.

Yet progress has been uneven. While many companies struggle to harness the power of these technologies, companies that are fully leveraging the capabilities are capturing disproportionate benefits, transforming their businesses and outpacing—and occasionally disrupting—the rest.

At the same time the technology itself continues to evolve rapidly, bringing new waves of advances in

  • robotics,
  • analytics,
  • and artificial intelligence (AI),
  • and especially machine learning.

Together they amount to a step change in technical capabilities that could have profound implications for business, for the economy, and more broadly for society as a whole. Machines today increasingly match or outperform human performance in a range of work activities, including ones that require cognitive capabilities, learning, making tacit judgments, sensing emotion, and even driving—activities that used to be considered safe from automation. Adoption of these technologies could bring significant new performance and transformational benefits to companies that go beyond simply substituting labor and lead to previously unimagined breakthrough performance and outcomes. Moreover, they have the potential to boost the productivity of the global economy at a time when it is sorely needed for growth and the share of the working-age population is declining.

Yet their advent raises difficult questions about how companies can best prepare for and harness these technologies, the skills and organizational reinvention that will be required to make the most of them, and how the leaders in the private and public sector as well as workers will adapt to the impact on jobs, capability-building and the nature of work itself.

Disruption

MGI-Briefing-Note-Automation-final

InsurTech Caught on the Radar

Publié le par hanswillertbleuazurconsultingeu

The overall picture that emerges from our InsurTech Radar is, first of all, that different business model categories vary significantly in terms of overall level of economic attractiveness and degree of startup activity. While some see little startup activity, others already appear overcrowded. The number of InsurTechs active in a category is not always in line with its relative attractiveness.

Secondly, even in the most attractive business model categories, it is not clear that InsurTechs will disrupt the industry and make the race. The players most likely to succeed vary by category. InsurTechs will not always be the winners. There are several categories in which either incumbents embracing digital change or firms from outside the insurance industry are most likely to succeed.

Thirdly, a number of underdeveloped categories present attractive opportunities. To be successful in these areas will require innovators to get creative on “demand side” thinking creating models that fundamentally change how risk coverage is presented and sold to customers, models that are not merely digital updates of traditional or slightly altered insurance propositions. Such thinking – substantially different from the “supply side” models of the current, first wave of InsurTechs – is essential for uncovering latent customer demand for risk cover.

Segment 1: Proposition

The proposition segment is less than half the size of the others. It is also the most varied in terms of outlook. The InsurTech Radar shows that there is currently a major mismatch in this segment between the categories with the highest level of startup activity and those with the greatest overall potential. Examples include Situational and Community-Based business model categories which we see as over-emphasized. Nevertheless, the proposition segment includes some of the most attractive categories of any InsurTech segment, as they represent true innovation on how risk coverage is presented and sold to customers – some of these currently see relatively little activity so far (such as the Risk Partner business model category). While the news here is good for established insurers, in that they are likely to be the winners in several of these attractive categories, it is also quite clear that InsurTechs are here to stay. The emergence of newly funded and fully digital insurance carriers might bring forward real breakthroughs. It is very likely that the segment will look quite different in a few years.

Segment 2: Distribution

The InsurTech Radar shows the distribution segment to be much better matched in terms of the level of activity and the categories with the highest likelihood of success. On the down side, all but two of these areas have, comparatively only moderate potential at best, due either to limited premium pools, challenges in sustaining value generation, little opportunity for differentiation, or some combination of these (such as B2C Online Brokers). As in the proposition segment, some of the most crowded categories are also likely to see a shakeout.

Segment 3: Operations

The operations segment is the most consistent of the three: Only one business model category here currently shows limited potential (the “Balance Sheet / Financial Resource
Management” category). Most others are highly attractive. InsurTechs are likely to dominate the segment, albeit sharing honors in the underwriting category with reinsurers.

InsurTech OW

Oliver_Wyman_and_Policen_Direkt_Global_InsurTech_Report_2017

How to successfully mitigate your organization’s third-party risk

Publié le par hanswillertbleuazurconsultingeu

What Is Third-Party Risk Management & Third-Party Due Diligence?

Third-party risk management is the process of assessing and controlling reputational, financial and legal risks to your organization posed by parties outside your organization. Third-party due diligence is the investigative process by which a third party is reviewed to determine any potential concerns involving legal, financial or reputational risks. Due diligence is disciplined activity that includes reviewing, monitoring and managing communication over the entire vendor engagement life cycle.

The Risks Are Real

As we see in the news too often, lapses in leadership around managing third parties have damaged organizations by exposing them to massive fines and penalties. According to the 2016 Benchmark Report, one-third of respondent organizations have faced legal or regulatory issues that involved third parties, with 50 percent of these involving average costs per incident of $10,000 or more. Even if the financial penalty can be managed, the reputational impact can have far-reaching consequences for many years. Third-party risk management is a top concern of compliance leaders, but many organizations are still coming to terms with how best to manage their third parties to limit risk and develop programs based on organizational risk assessments. The 2016 NAVEX Global benchmark report found that many organizations think they could be doing a better job of third-party risk management. Only 58 percent reported that they do a good job of complying with laws and regulations, and less than 25 percent rate their overall program as Good. Organizations may be diligent with their ethics and compliance programs, but for many the risk their third parties represent is a Wild West over which they feel like they have little control.

Benefits of a Strong Third-Party Risk Management Program

Managing third-party risk can make a big difference inhow well your organization can identify, manage and limit the liability a third party can represent. Your third party’s risk is your risk. You should have confidence that your program is minimizing that risk for you and your organization.

TPRClick here to access NAVEX detailed guide

How GRC Strategy & Integration Affects Confidence

Publié le par hanswillertbleuazurconsultingeu

Every organization does GRC whether they use the acronym or not. All have some approach to governing the organization, managing risk, and addressing compliance. It could be scattered in silos and disconnected, or it could be highly collaborated and integrated. Organizations should not be asking if they should do GRC but are to ask how mature their organization’s approach to GRC is and how it can be improved.

The formal definition for GRC found in the OCEG GRC Capability Model is that “GRC is a capability to reliably achieve objectives [governance] while addressing uncertainty [risk
management] and acting with integrity [compliance].” In the ideal world there is a natural flow through to GRC.

  • Governance sets objectives and directs and steers the organization setting the context for risk management.
  • Risk management aims to understand and minimize uncertainty in those objectives and reduce exposure to loss while maximizing performance.
  • Compliance assures that the organization operates with integrity to the boundaries established inorganization values, policies, regulatory and legal requirements, as well as boundaries set by risk limits and thresholds.

However, within many organizations there are often many GRC functions operating in isolation producing redundancy and gaps while remaining ignorant of the interrelationship of risk across silos. This has a measurable cost to the organization in
inefficiency, ineffectiveness, and lack of agility. Other organizations have mature and structured processes and reporting on GRC that brings together an integrated and
orchestrated view of GRC processes and information.

The goal of this 2017 OCEG GRC Maturity Survey report is to help organizations:

  • Understand the level of integration of GRC within organizations;
  • Differentiate the degree of confidence in performance with the ability to identify and manage risks and requirements;
  • Examine the benefits of an integrated GRC capability and the negative effects of siloed operations.

Integrated GRCClick here to access OCEG’s detailed analysis.

Achieving Optimal IFRS 9 Compliance

Publié le par testbacblog

IFRS 9 will have a substantial financial impact on banks and create implementation challenges. By taking an optimal approach to compliance, banks can balance the financial impact and the effort required and still ensure compliance. To achieve this goal, banks will need significant support from technology. In this paper, we explore the software functionality needed to support optimal IFRS 9 compliance for banks.

Across the globe, large financial institutions are working to understand the implications of the latest impairment requirements introduced by IASB1 as part of the IFRS 9 package. According to a recent Deloitte industry survey, this single, forward-looking “expected loss” impairment standard will have a significant financial impact for the majority of large banks.

Given that IFRS 9 requirements will be effective Jan. 1, 2018, banks are beginning to pay greater attention to this new accounting standard; IFRS 9 implementation budgets doubled during the last 12 months. But as discussed in this paper, any steps they take toward IFRS 9 compliance should not be taken in isolation, but rather in the context of existing regulatory pressures. With Basel III, CCAR, stress testing, BCBS 239 and other requirements, banks are already exposed to high levels of regulatory scrutiny and devoting substantial attention to compliance efforts.

Finally, it is expected that key jurisdictions will implement similar impairment approaches to IFRS 9, with the most relevant being the FASB’s Current Expected Credit Loss project. These initiatives will combine to broaden the scope of banks that need to implement ECL-based impairment approaches.

ifrs9

Click here to access SAS’ detailed analysis.

The Essential CIO Guide to Artificial Intelligence

Publié le par testbacblog

The topic of AI has reached such a fever pitch in the media with the coverage of driverless cars, conversational bots and even movies made by AI that it’s only a matter of time before every CEO starts asking their CIO “What’s our AI strategy.”

For many CIOs this will be a “deer in the headlights” moment since the topic of AI is so multi-faceted it’s hard to know where to start. We put together this e-book as a primer for CIOs wanting to get to grips with the topic of AI.
We start by giving some insight and context into why your CEO is asking this question, why now, and why you. Then, we will give you a foundational framework to think about AI so you can give your CEO a thoughtful response. Finally, we will discuss how you as CIO, can engage the business on the topic of AI and important considerations when evaluating AI vendors.
So, why is the CEO asking you this now? CEOs are humans too and they react to their environment. Their environment is often dominated by other CEOs, their board, and the outside world. AI as a topic has risen to the boardroom and the popular press with even Vanity Fair recently publishing an article titled “Suddenly Everyone is Obsessed with AI.” So if your CEO hasn’t broached the AI topic yet, they soon will.ai_breakthrough